Metaheuristic approach for optimizing neural networks parameters in battery state of charge estimation

• Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan Abdul, Abdul Aziz
• Format: Conference or Workshop Item
• Language: English; English
• Published: 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General); TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/38769/1/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters.pdf; http://umpir.ump.edu.my/id/eprint/38769/2/Metaheuristic%20approach%20for%20optimizing%20neural%20networks%20parameters%20in%20battery%20state%20of%20charge%20estimation_ABS.pdf; http://umpir.ump.edu.my/id/eprint/38769/; https://doi.org/10.1109/ISCAIE57739.2023.10164965

To accurately estimate the battery state of charge (SOC), it is vital to improve the performance of a battery-powered system. This paper employs the recent proposed Evolutionary Mating Algorithm (EMA) for optimizing the weights and biases of Feed-Forward Neural Network (FNN) in estimating the state...